User adaptive machines use sensor technology and computer algorithms to infer the user’s internal state and make decisions based on this information. Future cars could use this technology to intervene if the capacity of the driver to drive safely is degraded, even before performance starts to break down. The main challenge for such a support system is that it needs to interpret individual data automatically and immediately, while the individual is influenced by the same system. My thesis aims at this challenge, focussing on mental workload. In chapter three it is argued that a reliable system probably needs multiple types of measures to infer the user’s internal state, such as driving performance, physiology, and subjective experiences. The main result from chapter four is that users may use support actions of an adaptive system as a warning signal, and thereby not use the system as intended by the designers. In chapter five the potential was explored to use automatic music selection to influence mental workload was, but a direct link between mental effort and music type was not confirmed. Individual data analyses from brainwaves were the topic of chapter six, resulting in highly accurate workload classifications. This inspired the development of a performance and brain-based cruise control described in chapter seven. The adaptive performance of this system led to the conclusion that future research should focus on decreasing the context and time dependency of workload monitors for user adaptive systems.